Evaluating Ocean Wind Products for Short-range Forecast Analyses of Wind-Driven Surface Currents

Similar documents
Improvements to the Wind Driven Component of the OSCAR Surface Current Product

From El Nino to Atlantic Nino: pathways as seen in the QuikScat winds

El Niño, South American Monsoon, and Atlantic Niño links as detected by a. TOPEX/Jason Observations

EVALUATION OF THE GLOBAL OCEAN DATA ASSIMILATION SYSTEM AT NCEP: THE PACIFIC OCEAN

GAMINGRE 8/1/ of 7

OSSE OSE activities with Multivariate Ocean VariationalEstimation (MOVE)System. II:Impacts ofsalinity and TAO/TRITON.

GFDL, NCEP, & SODA Upper Ocean Assimilation Systems

Global Ocean Monitoring: Recent Evolution, Current Status, and Predictions

Applications of an ensemble Kalman Filter to regional ocean modeling associated with the western boundary currents variations

ECMWF: Weather and Climate Dynamical Forecasts

The 2010/11 drought in the Horn of Africa: Monitoring and forecasts using ECMWF products

Probabilistic predictions of monsoon rainfall with the ECMWF Monthly and Seasonal Forecast Systems

Global Ocean Monitoring: A Synthesis of Atmospheric and Oceanic Analysis

Feature resolution in OSTIA L4 analyses. Chongyuan Mao, Emma Fiedler, Simon Good, Jennie Waters, Matthew Martin

Faisal S. Syed, Shahbaz M.,Nadia R.,Siraj I. K., M. Adnan Abid, M. Ashfaq, F. Giorgi, J. Pal, X. Bi

Ocean Observations for an End-to-End Seasonal Forecasting System

The Arctic Energy Budget

Global climate predictions: forecast drift and bias adjustment issues

Fig.3.1 Dispersion of an isolated source at 45N using propagating zonal harmonics. The wave speeds are derived from a multiyear 500 mb height daily

ALASKA REGION CLIMATE OUTLOOK BRIEFING. June 22, 2018 Rick Thoman National Weather Service Alaska Region

Investigate the influence of the Amazon rainfall on westerly wind anomalies and the 2002 Atlantic Nino using QuikScat, Altimeter and TRMM data

The new ECMWF seasonal forecast system (system 4)

ENSO Outlook by JMA. Hiroyuki Sugimoto. El Niño Monitoring and Prediction Group Climate Prediction Division Japan Meteorological Agency

ALASKA REGION CLIMATE OUTLOOK BRIEFING. December 22, 2017 Rick Thoman National Weather Service Alaska Region

ALASKA REGION CLIMATE OUTLOOK BRIEFING. November 17, 2017 Rick Thoman National Weather Service Alaska Region

Applying Multi-Model Superensemble Methods to Global Ocean Operational Systems

Dynamical Statistical Seasonal Prediction of Atlantic Hurricane Activity at NCEP

Using Ensemble Sensitivity to Understand the Role of Sea Surface Temperatures in Midlatitude Storm Development in the Gulf Stream Region

A Statistical-Dynamical Seasonal Forecast of US Landfalling TC Activity

The Coupled Earth Reanalysis system [CERA]

Predictability of Sudden Stratospheric Warmings in sub-seasonal forecast models

ALASKA REGION CLIMATE FORECAST BRIEFING. October 27, 2017 Rick Thoman National Weather Service Alaska Region

particular regional weather extremes

An Overview of Atmospheric Analyses and Reanalyses for Climate

Figure 2. Time series of the standard deviation of spatial high pass filtered QuikSCAT wind stress (solid) and AMSR SST (dashed) for (top to bottom)

AMMA Conference 28 November-2 December, 2005 Dakar, Senegal. Multiyear Streamflow and Rainfall In the Ankobra River, Ghana

Diagnostic Model and Analysis of the Surface Currents in the Tropical Pacific Ocean

Long range predictability of winter circulation

On the presence of tropical vortices over the Southeast Asian Sea- Maritime Continent region

Modal view of atmospheric predictability

An extended re-forecast set for ECMWF system 4. in the context of EUROSIP

Developing Coastal Ocean Forecasting Systems and Their Applications

Atmospheric circulation analysis for seasonal forecasting

Impact of the Loss of QuikSCAT on NOAA NWS Marine Warning and

Invited paper on the South Atlantic J. Lutjeharms

Supplementary appendix

What is the difference between Weather and Climate?

Life Cycle of Convective Systems over Western Colombia

OPEC Annual Meeting Zhenwen Wan. Center for Ocean and Ice, DMI, Denmark

How DBCP Data Contributes to Ocean Forecasting at the UK Met Office

Introduction to Climate ~ Part I ~

AnuMS 2018 Atlantic Hurricane Season Forecast

SPECIMEN. Date Morning/Afternoon. A Level Geography H481/01 Physical systems Sample Question Paper. Time allowed: 1 hour 30 minutes PMT

Climate Prediction Center Research Interests/Needs

Tropical Pacific Ocean model error covariances from Monte Carlo simulations

Optimal Spectral Decomposition (OSD) for Ocean Data Analysis

The Oceanic Component of CFSR

Introduction to TIGGE and GIFS. Richard Swinbank, with thanks to members of GIFS-TIGGE WG & THORPEX IPO

QuikSCAT High Precision Wind Speed Cross Sections

Hazard assessment based on radar-based rainfall nowcasts at European scale The HAREN project

Tropical Indian Ocean Observing System - Present Status and Highlights of 2006 IOD -

AnuMS 2018 Atlantic Hurricane Season Forecast

ACC transport sensitivity to air-sea fluxes. Matt Mazloff SIO-UCSD

The U. S. Winter Outlook

A new global surface current climatology, with application to the Hawaiian Island region. Rick Lumpkin

Practice Test Chapter 8 Sinusoidal Functions

Exploring and extending the limits of weather predictability? Antje Weisheimer

APPLICATIONS OF DOWNSCALING: HYDROLOGY AND WATER RESOURCES EXAMPLES

Salem Economic Outlook

Coupled ocean-atmosphere ENSO bred vector

ALASKA REGION CLIMATE FORECAST BRIEFING. January 23, 2015 Rick Thoman ESSD Climate Services

3. Midlatitude Storm Tracks and the North Atlantic Oscillation

A Modeling Study on Flows in the Strait of Hormuz (SOH)

observational data analysis (Supplementary Material)

Dominant Anomaly Patterns in the Near-Surface Baroclinicity and. Accompanying Anomalies in the Atmosphere and Oceans. Part II: North.

Aquarius Data Release V2.0 Validation Analysis Gary Lagerloef, Aquarius Principal Investigator H. Kao, ESR And Aquarius Cal/Val Team

Validation and intercomparison for high resolution gridded product of surface wind vectors over the global ocean

Long Range Forecasts of 2015 SW and NE Monsoons and its Verification D. S. Pai Climate Division, IMD, Pune

Assessment of the Impact of El Niño-Southern Oscillation (ENSO) Events on Rainfall Amount in South-Western Nigeria

Seasonal Climate Watch April to August 2018

REPORT ON LABOUR FORECASTING FOR CONSTRUCTION

APPLICATIONS OF SEA-LEVEL PRESSURE

Satellite-Derived Surface Current Divergence in Relation to Tropical Atlantic SST and Wind

Skillful climate forecasts using model-analogs

Imperial College London

Effect of ocean surface currents on wind stress, heat flux, and wind power input to the ocean

Predicting Marine Physical-Biogeochemical Variability in the Gulf of Mexico and Southeastern U.S. Shelf Seas

Impact of frontal eddy dynamics on the Loop Current variability during free and data assimilative HYCOM simulations

North Atlantic circulation in three simulations of 1/12, 1/25, and 1/50

El Niño: How it works, how we observe it. William Kessler and the TAO group NOAA / Pacific Marine Environmental Laboratory

Role of Interannual Kelvin wave propagations in the equatorial Atlantic on the Angola-Benguela current system.

Short-Term Job Growth Impacts of Hurricane Harvey on the Gulf Coast and Texas

Background of Symposium/Workshop Yuhei Takaya Climate Prediction Division Japan Meteorological Agency

Time Series Analysis

over the Northern West Florida Shelf from SeaWinds and ASCAT

Jayalath Ekanayake Jonas Tappolet Harald Gall Abraham Bernstein. Time variance and defect prediction in software projects: additional figures

Variability and trend of the heat balance in the southeast Indian Ocean

Ultra-High Resolution Time Traveling AgMet Information from Seeding to Harvesting

Seasonal Climate Watch June to October 2018

The 2 nd phase of the Global Land-Atmosphere Coupling Experiment. Randal Koster GMAO, NASA/GSFC

Transcription:

OVWST Meeting November, p. Evaluating Ocean Wind Products for Short-range Forecast Analyses of Wind-Driven Surface Currents John T. Gunn, Kathleen Dohan, Gary S. E. Lagerloef, and Fabrice Bonjean Earth & Space Research Seattle, Washington

Introduction OVWST Meeting November, p. Ocean Surface Currents Analyses Real processing system (OSCAR) is a satellite-derived surface current database based on a combination of quasi-steady geostrophic and locally wind-driven dynamics (Bonjean and Lagerloef, ). The geostrophic term is computed from the gradient of surface topography fields (AVISO/CLS). Wind-driven velocity components are computed from an Ekman/Stommel formulation with variable viscosity using QuikSCAT winds (FSU/COAPS) with a thermal wind adjustment using satellite SST data. Data available at http://www.oscar.noaa.gov.

Introduction OVWST Meeting November, p. 3 The aim of this study is to evaluate the accuracy of NCEP Reanalysis- and ECMWF ERA- Reanalysis GCM products at measuring ocean surface winds through comparisons with mooring wind data and QuikSCAT winds to begin to asses the feasibility of using GCM forecasts for OSCAR forecast currents. Results presented here: comparisons of the three winds with the TAO/TRITON and PIRATA mooring arrays comparisons of the currents with drifters using the different winds in OSCAR. The ultimate goal is to extend OSCAR to real- and short-range forecast currents on a -5 day base which includes faster wind-driven currents.

OSCAR LATEST DEVELOPMENTS OVWST Meeting November, p. Oscar Currents Sep m/s Oscar Currents Sep 5 o N 5 o N m/s.. o N.5 o N.5 39 o N. 39 o N..3.3 3 o N. 3 o N. 33 o N m/s 7 o W 7 o W o W o W 5 o W o W. 33 o N m/s 7 o W 7 o W o W o W 5 o W o W. Newest development of OSCAR: increase resolution from one degree to /3 degree. Smaller-scale features in higher resolution model, e.g. Gulf Stream region. Much closer to coasts with optimal coverage at each step. Improved model for equatorial currents.

COMPARISON WITH DRIFTERS OVWST Meeting November, p. 5 Currents are interpolated onto the drifter locations (averaged over day). Zonal and meridional currents vs drifter velocities. We want to incorporate faster wind-driven mixed layer currents than our current quasi-steady model allows. This requires an extension from 5-day to -day base and -dependent terms in the momentum equations.

QuikSCAT Comparisons with moorings OVWST Meeting November, p. Time series of mooring at (5 o W, o N) and QuikSCAT data over months. U (top) and V (bottom) Mooring (blue) and QuikSCAT (black). U (m/s) V (m/s) QuikSCAT Zonal Winds vs TAO for year at mooring 5w n QuikSCAT Mooring / /9 / /3 /3 / /3 / /7 QuikSCAT Meridional Winds vs TAO for year at mooring 5w n / /9 / /3 /3 / /3 / /7

All winds comparisons with moorings U (m/s) V (m/s) QuikSCAT Zonal Winds vs TAO for year at mooring 5w n QuikSCAT Mooring / /9 / /3 /3 / /3 / /7 QuikSCAT QuikSCAT Meridional Winds vs TAO for year at mooring 5w n / /9 / /3 /3 / /3 / /7 U (m/s) NCEP Zonal Winds vs TAO for year at mooring 5w n NCEP / /9 / /3 /3 / /3 / /7 U (m/s) V (m/s) ECMWF Zonal Winds vs TAO for year at mooring 5w n ECMWF Mooring / /9 / /3 /3 / /3 / /7 ECMWF ECMWF Meridional Winds vs TAO for year at mooring 5w n / /9 / /3 /3 / /3 / /7 NCEP Mooring V (m/s) NCEP Meridional Winds vs TAO for year at mooring 5w n / /9 / /3 /3 / /3 / /7 OVWST Meeting November, p. 7

All winds comparisons with moorings U (m/s) QuikSCAT Zonal Winds vs TAO for year at mooring 5w n QuikSCAT Jan Feb Mar Apr May Jun Jul QuikSCAT Mooring U (m/s) ECMWF Zonal Winds vs TAO for year at mooring 5w n ECMWF Jan Feb Mar Apr May Jun Jul ECMWF Mooring V (m/s) QuikSCAT Meridional Winds vs TAO for year at mooring 5w n Jan Feb Mar Apr May Jun Jul U (m/s) NCEP Zonal Winds vs TAO for year at mooring 5w n NCEP Jan Feb Mar Apr May Jun Jul V (m/s) ECMWF Meridional Winds vs TAO for year at mooring 5w n Jan Feb Mar Apr May Jun Jul NCEP Mooring V (m/s) NCEP Meridional Winds vs TAO for year at mooring 5w n Jan Feb Mar Apr May Jun Jul OVWST Meeting November, p.

OVWST Meeting November, p. 9 Correlation QuikSCAT U CORRELATION day 5 5 3 35.5 QuikSCAT V CORRELATION day 5 5 3 35.5 ECMWF U CORRELATION day 5 5 3 35.5 ECMWF V CORRELATION day 5 5 3 35.5 NCEP U CORRELATION day 5 5 3 35 longitude ( o E).5 NCEP V CORRELATION day 5 5 3 35 longitude ( o E).5 Correlations with each mooring. The products are interpolated onto each mooring location at the mooring s. Statistics are calculated from -Jul-999 to 3-Aug-. U component on the left, V on the right. QuikSCAT, ECMWF, NCEP

OVWST Meeting November, p. SKILL QuikSCAT U SKILL day 5 5 3 35 QuikSCAT V SKILL day 5 5 3 35 ECMWF U SKILL day 5 5 3 35 ECMWF V SKILL day 5 5 3 35 NCEP U SKILL day 5 5 3 35 longitude ( o E) NCEP V SKILL day 5 5 3 35 longitude ( o E) SKILL = std(u mooring U product ) std(u mooring )

OVWST Meeting November, p. Relative Standard Deviation QuikSCAT U RSTD day 5 5 3 35 QuikSCAT V RSTD day 5 5 3 35 ECMWF U RSTD day 5 5 3 35 ECMWF V RSTD day 5 5 3 35 NCEP U RSTD day 5 5 3 35 longitude ( o E) NCEP V RSTD day 5 5 3 35 longitude ( o E) Relative standard deviation is a measure of the comparison between variances of products. RSTD = std(u mooring) std(u product ) std(u mooring )

OVWST Meeting November, p. Overall Score T = day CorrU CorrV SkillU Skill V Rstdu Rstdv ScoreU ScoreV QuikSCAT.9.9.57.5 -. -. 7.. ECMWF.9.7.5.9.. 7.3.3 NCEP.5.55 -.7 -. -.5 -. 3. 3.3 T = 5days CorrU CorrV SkillU Skill V Rstdu Rstdv ScoreU ScoreV QuikSCAT.9.93.5.57 -. -.5 7.3.59 ECMWF.95.9.9..7.3 7. 7.39 NCEP.7.77..7 -.7 -.3.9.97 Final score (out of ) reflects the overall ability to match the mooring data. SCORE = ( + COR) ( + SKILL) + RSTD.5 T = 5 days indicates statistics for data mapped onto OSCAR base: 5-day and smoothed over days.

TOWARDS FASTER TIMESCALES OVWST Meeting November, p. 3 OSCAR currents are calculated on a -day base using -day winds with interpolated SSH and SST values. No acceleration term included in the model. Sample output. Comparison with drifters: Data from drifters in the box are averaged over one day. Model currents are interpolated onto the drifter locations at each day. Statistics are calculated on the ensemble of points during -Mar- to 3-May-.

COMPARISON WITH DRIFTERS N=37 rmsd=5cm/s rdstd=7% md=.3cm/s cor=.3 sk=. sco=3.3 N=37 rmsd=cm/s rdstd=% md=.3cm/s cor=. sk=. sco=3.5 Zonal and meridional OSCAR currents using QuikSCAT winds vs drifter velocities. Results are very similar between winds. Corr Skill Score Zonal OSCARQSCD vel (cm/s) 5 Meridional OSCARQSCD vel (cm/s) u.3. 3.35 v.. 3.53 5 5 5 Zonal Drifter vel (cm/s) 5 5 Meridional Drifter vel (cm/s)........ Frequency (no unit)... Frequency (no unit)......... 5 5 5 5 Zonal [OSCARQSCD Drifter] vel (cm/s) (bar width = 5 cm/s) 5 5 5 5 Meridional [OSCARQSCD Drifter] vel (cm/s) (bar width = 5 cm/s) OVWST Meeting November, p.

COMPARISON WITH DRIFTERS OVWST Meeting November, p. 5 T = day CorrU CorrV SkillU Skill V Rstdu Rstdv ScoreU ScoreV QuikSCAT.3....7. 3.35 3.53 ECMWF..7...7. 3.37 3.5 NCEP..3.9..7. 3. 3.5 T = 5days CorrU CorrV SkillU Skill V Rstdu Rstdv ScoreU ScoreV QuikSCAT.57.3..3.9. 3. 3.3 Better results for increased resolution is promising for extending surface currents beyond the quasi-steady 5-day model.

Future Work OVWST Meeting November, p. We have presented a preliminary assessment of QuikSCAT, ECMWF and NCEP winds for the purpose of an improved wind-driven component to the OSCAR system. More analyses will be performed, in particular spatial comparisons (e.g. comparisons of EOF structures) and evaluation of forecast products. Goals: include -dependent dynamics in OSCAR to include high-frequency wind-driven currents extend OSCAR capability to nowcast and forecast improve the wind-driven turbulent mixing scheme.